PyTorch Tensor Corruption Bug: Failed Resizes

Dec 16, 2025 by Alex Johnson 46 views

Have you ever encountered a mysterious crash in your PyTorch code, perhaps a Segmentation Fault or an elusive RuntimeError that seems to come out of nowhere? If you're working with tensors that might share storage with non-resizable buffers, you might be running into a subtle but problematic bug within PyTorch itself. This issue, where PyTorch updates tensor shape metadata even when a storage resize fails, can leave your tensors in a corrupted state, often referred to as a "Zombie" tensor. Let's break down what's happening, why it's a problem, and how it impacts your machine learning workflows.

Understanding the Core Problem: The Unsafe Resize

At the heart of this bug lies the resize_() operation in PyTorch. When you call resize_() on a tensor, PyTorch attempts to allocate new memory or adjust the existing memory buffer to accommodate the new dimensions you specify. However, things get tricky when a tensor's storage is backed by something that cannot be resized. A prime example of this is when you inject a NumPy array into a PyTorch tensor using set_(). NumPy arrays, once created, generally have fixed-size storage. If you then try to resize_() a PyTorch tensor that's using this fixed-size NumPy array as its backing storage, PyTorch should detect this incompatibility and raise an error. And indeed, it does! You'll typically see a RuntimeError with a message like: "Trying to resize storage that is not resizable."

This error message is crucial because it indicates PyTorch knows it can't perform the resize. However, the problem isn't with detecting the failure, but with how the operation handles it. The resize_() method is not exception-safe. This means that before it fully checks if the storage is resizable, it goes ahead and updates the tensor's shape and stride metadata to match the target size you requested. So, even though the RuntimeError is eventually raised, the tensor's internal pointers and size information have already been modified. This leaves the tensor in a precarious and inconsistent state. It thinks it has a certain shape (e.g., a 5x5x5 tensor), but its actual underlying storage is still the original, often empty or much smaller, non-resizable buffer (e.g., 0 bytes).

This corrupted state is what we're calling a "Zombie" tensor. It has the appearance of a valid tensor with a specific shape, but its actual data buffer is incompatible or non-existent. The consequences of this can be severe. If your code continues to execute after this exception has been caught (perhaps within a try-except block), and you then attempt to access or print the contents of this "Zombie" tensor, you're very likely to encounter a crash. This could manifest as a Segmentation Fault, which is a low-level error indicating your program tried to access memory it shouldn't have, or another internal RuntimeError as PyTorch tries to reconcile the conflicting metadata and storage. The minimal reproduction case provided clearly demonstrates this: a tensor is created with empty storage, resize_() is called with a new shape, the RuntimeError is caught, but the t.shape now reflects the new size (5, 5, 5) while t.untyped_storage().nbytes() remains 0. Printing this tensor then leads to the observed crash.

Why This Matters: Impact on Machine Learning Workflows

This bug, while seemingly specific to a niche scenario, can have significant repercussions for anyone developing and deploying machine learning models using PyTorch. Reproducibility and stability are paramount in scientific computing and deep learning. When your framework introduces subtle corruptions that lead to unpredictable crashes, it erodes confidence in your tools and makes debugging a nightmare. Imagine training a large model for hours or days, only for it to crash due to a tensor that ended up in this "Zombie" state because of an intermediate, unhandled exception during a resize operation. The lost time and effort can be substantial.

Furthermore, this issue highlights the importance of exception safety guarantees in software libraries. For operations that might fail, it's essential that they either succeed cleanly or leave the system in the state it was before the operation began. This is known as the Strong Exception Guarantee. In this case, PyTorch provides only a Weak Exception Guarantee: the operation fails, but it leaves the system (the tensor's metadata) in a modified, inconsistent state. For numerical computations and deep learning, where tensors represent the core data structures, such inconsistencies can propagate silently until they cause a catastrophic failure.

Consider scenarios where tensors are dynamically resized or reshaped within training loops, data augmentation pipelines, or during model inference. If any of these operations involve tensors with shared, non-resizable storage (which might arise unexpectedly from interactions with libraries like NumPy or custom C++ extensions), the risk of hitting this bug increases. The "Zombie" tensor state means that even if you catch the initial RuntimeError, the tensor itself is fundamentally broken. Any subsequent operations that depend on its shape or try to access its (non-existent) data will likely fail.

This problem underscores the need for robust error handling and thorough testing of edge cases within deep learning frameworks. Developers need to be aware that seemingly straightforward operations like resize_() can have hidden pitfalls when combined with specific storage sharing mechanisms. The lack of a clear exception-safe behavior here means that developers must be extra vigilant, potentially adding their own checks or workarounds to prevent tensors from entering this corrupted state. The provided minimal reproduction code is a valuable tool for understanding and demonstrating the bug, allowing developers to test potential fixes and ensure their code is resilient to such issues. The ultimate goal is to ensure that PyTorch operations provide strong guarantees, preventing such data corruption and maintaining the integrity of machine learning computations.

Minimal Reproduction Case: Seeing the Bug in Action

To truly grasp the severity and nature of this PyTorch bug, let's dissect the minimal reproduction code provided. This snippet is invaluable because it isolates the exact conditions that trigger the issue, making it easier to understand and, hopefully, to fix.

import torch
import numpy as np

# Create non-resizable storage (0 bytes)
locked_storage = torch.from_numpy(np.array([], dtype=np.int32)).untyped_storage()

# Inject into a fresh tensor
t = torch.tensor([], dtype=torch.int32)
t.set_(locked_storage)

# Attempt to resize (Expected: Fail, maintain original shape)
# (Actual: Fails, but updates shape to 5x5x5)
try:
    t.resize_((5, 5, 5))
except RuntimeError:
    pass

# Verify corruption
print(f"Shape: {t.shape}")
print(f"Storage: {t.untyped_storage().nbytes()}")
print(t) # CRASH

The code starts by creating a locked_storage. This is done by converting an empty NumPy array ( np.array([], dtype=np.int32) ) into a PyTorch tensor and then extracting its untyped_storage(). The key here is that the NumPy array has a fixed, non-resizable memory block associated with it. When this storage is used by a PyTorch tensor, any attempt to resize that tensor's storage will fail.

The next step is to create a new, empty PyTorch tensor (t = torch.tensor([], dtype=torch.int32)). Immediately after, t.set_(locked_storage) is called. This is where the tensor t is made to point to the locked_storage. Now, t is a tensor whose underlying data buffer is the unresizable one we created.

The crucial part is the try-except block. Inside it, t.resize_((5, 5, 5)) is attempted. As expected, since locked_storage is not resizable, PyTorch correctly raises a RuntimeError. However, the bug lies in the sequence of events before the exception is fully handled. PyTorch, unfortunately, updates the tensor's shape and stride metadata to torch.Size([5, 5, 5]) before it confirms that the storage itself can be resized. When the RuntimeError is raised and caught by our except block, the operation stops, but the metadata has already been altered.

The verification step starkly illustrates the corruption. When we print t.shape, it reports torch.Size([5, 5, 5]), exactly as we requested in resize_(). However, printing t.untyped_storage().nbytes() reveals that the storage size is still 0. This is the "Zombie" state: the tensor thinks it has data in a 5x5x5 shape, but its actual storage buffer is empty and cannot hold any data. The final print(t) line is where the program typically crashes. PyTorch attempts to access the tensor's data based on the torch.Size([5, 5, 5]) metadata, but finds no actual data in the underlying 0-byte storage, leading to a segmentation fault or another runtime error.

The expected behavior, as noted in the bug report, is that if resize_() fails due to locked storage, the tensor's metadata should remain unchanged, ideally retaining its original torch.Size([0]). This would ensure a strong exception guarantee, meaning the operation either succeeds or leaves the tensor in its original, consistent state. The current behavior, however, violates this principle, leading to unstable and unpredictable program execution. Understanding this minimal example is key to developing robust solutions and contributing to the stability of the PyTorch ecosystem.

Potential Fixes and Mitigation Strategies

Addressing the "Zombie" tensor bug requires ensuring that PyTorch operations uphold strong exception guarantees, especially when dealing with mutable tensor properties like shape and storage. The core issue is that the tensor's metadata is updated before the success of the underlying storage operation is confirmed. A robust fix would involve reordering these steps.

One primary approach is to perform the storage resize check and allocation first. Only if the storage can be successfully resized (or if new storage is allocated) should the tensor's shape and stride metadata be updated. If the storage resize fails, the operation should be immediately aborted, and the tensor's metadata should remain entirely untouched. This aligns with the principle of strong exception safety, ensuring that a failed operation does not leave the object in a corrupted or partially modified state.

Specifically, within the Tensor::resize() method (or its equivalent internal implementation), the logic should be refactored. Before modifying self.shape_ or self.strides_, the code needs to definitively confirm that self.storage_.unsafe_set_size(new_size) (or a similar internal mechanism for managing storage capacity) has succeeded. If unsafe_set_size fails and throws an exception, that exception should propagate without any prior modification of the tensor's shape or stride attributes.

For developers encountering this issue, several mitigation strategies can be employed in the short term:

Avoid resize_() on Tensors with Shared Non-Resizable Storage: Be mindful of tensors created from NumPy arrays or other sources with fixed storage. If such a tensor needs to change shape, consider creating a new tensor with the desired shape and copying the data, rather than attempting an in-place resize.
Thorough Exception Handling: Wrap operations that might involve resize_() on potentially shared storage in try-except blocks. However, recognize that simply catching the error might not be enough if the tensor remains corrupted. You might need to immediately discard or re-initialize the tensor after an exception.
Explicit Copying: If you suspect a tensor might be affected, explicitly create a copy of it using tensor.clone() or tensor.detach().clone() before performing potentially problematic operations. This ensures that the original tensor's metadata and storage remain intact, and you are working with a new, independent tensor.
Runtime Checks: After operations that could potentially trigger this bug (especially if they were inside a try-except block), add explicit checks. For instance, verify that tensor.numel() == tensor.storage().nbytes() // tensor.element_size() or that tensor.shape.numel() * tensor.element_size() == tensor.storage().nbytes(). If these invariants are violated, you've likely hit the "Zombie" state.

Long-term, the ideal solution lies within the PyTorch core library. Implementing a strict check-then-modify sequence for resize_() operations is crucial. This might involve ensuring that all necessary storage checks and adjustments are completed successfully before any metadata associated with the tensor's dimensions is altered. This dedication to strong exception guarantees will significantly improve the robustness and reliability of PyTorch for all users.

Conclusion: Towards More Robust PyTorch Tensors

The bug where PyTorch updates tensor shape metadata even when storage resize fails is a critical issue that can lead to unpredictable crashes and data corruption. It stems from a violation of strong exception safety guarantees, leaving tensors in an inconsistent "Zombie" state. By understanding the mechanics of the resize_() operation and its interaction with non-resizable storage, developers can better anticipate and mitigate these problems.

While the provided minimal reproduction case clearly illustrates the problem, the implications extend to various machine learning workflows where dynamic tensor manipulation is common. The path forward involves both vigilance from users – employing careful coding practices and thorough testing – and crucial improvements within the PyTorch framework itself, focusing on robust exception handling and ensuring that operations either succeed completely or leave data structures entirely unmodified.

For further insights into tensor operations and memory management in PyTorch, you can explore the official PyTorch documentation and discussions on GitHub. Understanding these underlying mechanisms is key to building reliable and efficient deep learning applications.